Curated Reading
Resources
External material I'd actually point students at — the books, courses, code, and visualizations that are better than anything I could re-write here. Each subject area below has its own curated list with notes on what each resource is best for.
Mathematics
Linear algebra, calculus, probability, statistics, optimization, information theory — the foundations ML stands on.
FoundationsCode & Implementation
Library docs, worked examples, and famous codebases worth reading. Where to find well-tested implementations.
Libraries & reposCourses
Structured online courses — from gentle introductions to grad-level specialty topics.
CurriculaReference Textbooks
The canonical references for when the TL;DRs aren't enough. Many are free online.
Books